請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/68887完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳信希 | |
| dc.contributor.author | Yu-Hsiang Huang | en |
| dc.contributor.author | 黃宇祥 | zh_TW |
| dc.date.accessioned | 2021-06-17T02:40:25Z | - |
| dc.date.available | 2018-08-18 | |
| dc.date.copyright | 2018-08-18 | |
| dc.date.issued | 2017 | |
| dc.date.submitted | 2017-08-16 | |
| dc.identifier.citation | Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio, “Neural Machine Translation by Jointly Learning to Align and Translate”, Proceedings of International Conference on Learning Representations, 2015.
Francesco Barbieri and Horacio Saggion, “Modelling Irony in Twitter”, Proceedings of the Student Research Workshop at the 14th Conference of the European Chapter of the Association for Computational Linguistics, pp.56-64, 2014. Francesco Barbieri, Horacio Saggion, and Francesco Ronzano. “Modelling Sarcasm in Twitter, a Novel Approach”, Proceedings of the Association for Computational Linguistics, pp.50-58, 2014. Junyoung Chung, Caglar Gulcehre, KyungHyun Cho and Yoshua Bengio, 'Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling”, Proceedings of the Advances in Neural Information Processing Systems Deep Learning and Representation Learning Workshop, 2014. Misha Denil, Loris Bazzani, Hugo Larochelle, and Nando de Freitas, “Learning where to attend with deep architectures for image tracking”, Neural Computation, 24(8), pp.2151–2184, 2012. Chris Dyer, Miguel Ballesteros, Wang Ling, Austin Matthews, and Noah A. Smith, “Transition-based dependency parsing with stack long short-term memory”, Proceedings of the Association for Computational Linguistics, 2015. Debanjan Ghosh, Weiwei Guo and Smaranda Muresan, “Sarcastic or Not: Word Embeddings to Predict the Literal or Sarcastic Meaning of Words”, Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp.1003-1012, 2015. Ross Girshick, Jeff Donahue, Trevor Darrell and Jitendra Malik, “Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation”, Proceedings of The IEEE Conference on Computer Vision and Pattern Recognition, pp.580-587, 2014. Roberto González-Ibáñez, Smaranda Muresan, and Nina Wacholder, “Identifying sarcasm in twitter: A closer look”, Proceedings of the Association for Computational Linguistics, pp.581–586, 2011. Sepp Hochreiter and Jürgen Schmidhuber, “Long Short-Term Memory”. Neural computation, 9 (8), pp.1735-1780, 1997. Aminul Islam and Diana Inkpen, “Semantic text similarity using corpus-based word similarity and string similarity”, ACM Transactions on Knowledge Discovery from Data (TKDD), 2(2):10, 2008. A. Joulin, E. Grave, P. Bojanowski and T. Mikolov, “Bag of Tricks for Efficient Text Classification”, ArXiv preprint arXiv:1607.01759, 2016. Mikhail Khodak, Nikunj Saunshi and Kiran Vodrahalli, “A Large Self-Annotated Corpus for Sarcasm”, ArXiv preprint, https://arxiv.org/pdf/1704.05579.pdf, 2017. Yoon Kim, “Convolutional Neural Networks for Sentence Classification”, Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, pp.1746-1751, 2014. Diederik Kingma and Jimmy Ba, “Adam: A method for stochastic optimization”, Proceedings of the 3rd International Conference for Learning Representations, abs/1412.6980, 2014. Roger Kreuz and Sam Glucksberg: How to be Sarcastic, “The Echoic Reminder Theory of Verbal Irony”, Journal of Experimental Psychology General, 118(4), pp.374-386, 1989. Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton, “ImageNet classification with deep convolutional neural networks”, Proceedings of the Advances in Neural Information Processing Systems, pp. 1106–1114, 2012. Y. LeCun, L. Jackel, L. Bottou, A. Brunot, C. Cortes, J. Denker, H. Drucker, I. Guyon, U. Mller, E. Sckinger, P. Simard, and V. Vapnik, “Comparison of Learning Algorithms for Handwritten Digit Recognition”, Proceedings of the International Conference on Artificial Neural Networks, pp.53-60, 1995. Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S. Corrado, and Jeff Dean, “Distributed Representations of Words and Phrases and Their Compositionality”, Proceedings of the Advances in Neural Information Processing Systems, pp.3111-3119, 2013. Volodymyr Mnih, Nicolas Heess, Alex Graves and Koray Kavukcuoglu, “Recurrent Models of Visual Attention”, Proceedings of the Advances in Neural Information Processing Systems, pp. 2204-2212, 2014. Antonio Reyes, Paolo Rosso and Tony Veale, “A Multidimensional Approach for Detecting Irony in Twitter”, Language Resources and Evaluation, 47(1), pp.239-268, 2013. Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever and Ruslan Salakhutdinov, “Dropout: A simple way to prevent neural networks from overfitting”, The Journal of Machine Learning Research, 15(1), pp.1929-1958, 2014. Yi-Jie Tang and Hsin-Hsi Chen, “Chinese Irony Corpus Construction and Ironic Structure Analysis”, Proceedings of the 25th International Conference on Computational Linguistics, pp.1269-1278, 2014. Meishan Zhang, Yue Zhang and Guohong Fu, “Tweet Sarcasm Detection Using Deep Neural Network”, Proceedings of the 26th International Conference on Computational Linguistics, pp. 2449- 2460, 2016. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/68887 | - |
| dc.description.abstract | 自動反諷偵測，旨在使電腦理解人類反諷文字背後的真實意圖。過去的研究者 嘗試諸多人工抽取的複雜特徵，與各式經典機器學習方法。本研究探討如何通過詞 嵌入和深層神經網路將深度學習模型應用於此任務。本研究使用三種不同的深度 學習模型，分別為卷積神經網絡、循環神經網絡、和具注意力機制的循環神經網絡。 結果顯示具注意力機制的循環神經網絡在無脈絡的 Twitter 資料集和具脈絡的 Reddit 資料集達到最好的表現。此外，通過觀察由具注意力機制的循環神經網絡產 生的注意力權重向量，試圖窺見注意力機制如何幫助深度學習模型找出反諷語言 的文字線索。 | zh_TW |
| dc.description.abstract | Automatic Irony Detection refers to making the computer understand the real intentions of the human behind the ironic language. Much work has been done using classic machine learning techniques together with various features. In contrast to sophisticated feature engineering, this research investigates how deep learning can be applied to the Automatic Irony Detection task with the help of word embedding. Three different deep learning models, including Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Attentive RNN, are explored. It shows that the Attentive RNN achieves the state-of-the-art performance on contextless and contextualized dataset. Furthermore, with a closer look at the attention vectors generated by Attentive RNN, an insight into how the attention mechanism helps find out the linguistic clues of ironic utterances is provided. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-17T02:40:25Z (GMT). No. of bitstreams: 1 ntu-106-R04922095-1.pdf: 2043756 bytes, checksum: dd9b8658988589920954203a674e2d0a (MD5) Previous issue date: 2017 | en |
| dc.description.tableofcontents | 口試委員會審定書 i
誌謝 ii 中文摘要 iii Abstract iv Contents v List of Figures viii List of Tables ix Chapter 1 Introduction 1 1.1 Motivation 1 1.2 Thesis Organization 2 Chapter 2 Related Work 3 2.1 Irony detection 3 2.1.1 Data Resource 3 2.1.2 Labeling 3 2.1.3 Contextless or Contextualized 4 2.1.4 Detection Approaches 4 2.2 Deep Learning 5 2.2.1 Convolutional Neural Network 5 2.2.2 Recurrent Neural Network 6 2.2.3 Attention Mechanism 7 Chapter 3 Methods 8 3.1 Framework 8 3.2 Encoding Methods 8 3.2.1 Convolutional Neural Network 8 3.2.2 Recurrent Neural Network 10 3.2.3 Attentive Recurrent Neural Network 11 3.2.4 Modification for Context Encoding 12 3.3 Project to Target Space 13 Chapter 4 Datasets 14 4.1 Twitter: A Contextless Dataset 14 4.1.1 Data Source 14 4.1.2 Dataset Collection 15 4.1.3 Basic Statistics of the Dataset 16 Figure 4‑2. Ratio of sarcastic/non-sarcastic instances in training data 16 4.2 Reddit: A contextualized dataset 17 4.2.1 Data Source 17 4.2.2 Dataset Collection 18 4.2.3 Basic Statistics of the Dataset 19 Chapter 5 Experiments 21 5.1 Contextless Experiment 21 5.1.1 Preprocessing 21 5.1.2 Baseline 21 5.1.3 Evaluation 22 5.1.4 Parameter Settings 22 5.2 Contextualized Experiment 23 5.2.1 Preprocessing 23 5.2.2 Baseline 23 5.2.3 Evaluation 24 5.2.4 Parameter Settings 24 Chapter 6 Discussion 26 6.1 Attention Weight Plots 26 6.1.1 Twitter 26 6.1.2 Reddit 27 6.2 The Issues of the Contextualization 29 6.3 The Correlation with the Metadata 32 6.3.1 Voted Score 32 6.3.2 Topic 33 6.4 Word Embedding Pre-training 34 6.5 Error Analysis 35 6.5.1 Inaccessible Context 35 6.5.2 Noisy Self-Annotated Label 36 Chapter 7 Conclusion 37 References 38 | |
| dc.language.iso | en | |
| dc.subject | 注意力機制 | zh_TW |
| dc.subject | 循環類神經網路 | zh_TW |
| dc.subject | 反諷偵測 | zh_TW |
| dc.subject | Recurrent Neural Network | en |
| dc.subject | Attention Mechanism | en |
| dc.subject | Irony Detection | en |
| dc.title | 基於具注意力機制類神經網路的反諷偵測 | zh_TW |
| dc.title | Irony Detection Using Attentive Neural Network | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 105-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 林川傑,禹良治,蔡宗翰 | |
| dc.subject.keyword | 反諷偵測,循環類神經網路,注意力機制, | zh_TW |
| dc.subject.keyword | Irony Detection,Recurrent Neural Network,Attention Mechanism, | en |
| dc.relation.page | 38 | |
| dc.identifier.doi | 10.6342/NTU201703535 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2017-08-17 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 資訊工程學研究所 | zh_TW |
| 顯示於系所單位： | 資訊工程學系 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-106-1.pdf 未授權公開取用 | 2 MB | Adobe PDF |
系統中的文件，除了特別指名其著作權條款之外，均受到著作權保護，並且保留所有的權利。